The present invention relates generally to the field of medical imaging. In particular, the following techniques relate to computed tomography imaging systems and the calibration of detectors used in such systems.
Computed tomography (CT) imaging systems measure the attenuation of X-ray beams passed through a patient from numerous angles. Based upon these measurements, a computer is able to reconstruct images of the portions of a patient's body responsible for the radiation attenuation. As will be appreciated by those skilled in the art, these images are based upon separate examination of a series of angularly displaced projection images. A CT system produces data that represents the line integral of linear attenuation coefficients of the scanned object. This data is then reconstructed to produce an image, which is typically displayed on a cathode ray tube, and may be printed or reproduced on film. A virtual 3-D image may also be produced by a CT examination.
CT scanners operate by projecting fan shaped or cone shaped X-ray beams from an X-ray source that is collimated and passes through the object, such as a patient. The attenuated beams are then detected by a set of detector elements. The detector element produces a signal based on the intensity of the X-ray beams, which are attenuated by the patient, and the data are processed to produce signals that represent the line integrals of the attenuation coefficients of the object along the ray paths. These signals are typically called projections. By using reconstruction techniques, such as filtered backprojection, useful images are formulated from the projections. The locations of pathologies may then be identified either automatically, such as by a computer-assisted diagnosis (CAD) algorithm or, more conventionally, by a trained radiologist. CT scanning provides certain advantages over other types of techniques in diagnosing disease particularly because it illustrates the accurate anatomical information about the body. Further, CT scans may help physicians distinguish between types of abnormalities more accurately.
In order to accurately reconstruct CT images, the spectral response of each detector element is calibrated to a standardized spectral response. Spectral calibration is useful in removing artifacts, such as beam-hardening artifacts, which may occur when tissue is imaged. In particular, the beam-hardening phenomena may cause nonuniformities in a reconstructed image of a uniform object, such as the phantoms used in calibration. This phenomenon, known as “cupping effect,” arises due to the polychromatic nature of the X-ray beam and the resulting differential absorption of high and low energy components of the beam. Correction factors to the projection data may be applied to remove the beam hardening artifacts in the reconstructed image.
One method to determine the correction factors is through empirical experimentation. The determination of the correction factors can be made though either system modeling with a nominal detector spectral response or though experiments by adjusting the correction coefficients such that the reconstructed image of a water phantom, for example, becomes uniform. With such a beam hardening spectral correction, the resulting reconstructed image, however, may still contain ring or band artifacts due to the differential spectral response of the various detector elements. The term “spectral error,” as used herein, refers to the differential detector spectral response as compared to a nominal channel responding to an incident polychromatic x-ray spectrum. The spectral calibration process generates correction functions for each detector element to balance channel-to-channel response to an X-ray signal, thereby removing the ring or band artifacts. Various factors, however, may result in the derivation of correction functions from the spectral calibration process that are insufficient or inadequate to fully remove artifacts in the reconstructed image resulting from differential detector element response. In particular, the techniques employed to derive correction functions typically rely upon an insufficient number of data points representing the spectral response of an element as a function of projection value. As a result, the correction function for detector channel-to-channel variation in spectral response may be substantially linear, even though the spectral response of a detector element as a function of projection value is not necessarily linear.
For example, spectral calibration may be performed by positioning a circular phantom at the isocenter of an imaging system. The symmetry of the phantom in conjunction with the position at the isocenter results in attenuation data being collected which provides limited detector channel coverage and which corresponds to a single penetration length, i.e., the distance traversed by the X-rays through the attenuating object. As a result, calibration data are acquired at each detector element for a single penetration length for each phantom and do not provide information about detector spectral response as a function of X-ray penetration length. A calibration may be performed using a second phantom to acquire a data point at a second penetration length for each detector element such that a linear correction function may be derived. Likewise additional calibrations may be performed to provide additional data points. The resulting correction function, however, is generally substantially linear and may fail to adequately correct the differential spectral responses of the detector elements to the extent that such responses are non-linear away from the measured data points. In addition, the image regions corresponding to the joining of the different data sets may give rise to image artifacts.
One technique, which may be employed to address these concerns, includes smoothing projections from the measured phantom calibration data to extract baseline projections, which may be used to determine correction factors for each detector element. In particular, the smoothed projection may be considered an ideal projection to which the detector elements may be calibrated. This technique, however, may be unreliable if the detector elements differentially introduce large relative spectral errors, which influence the computation of the smoothed projections, resulting in the extraction of an incorrect baseline. A technique for measuring spectral errors as a function of X-ray projection value and of calibrating detector elements to reduce the incidence of image artifacts attributable to differential channel-to-channel spectral response is therefore desirable.